I'm always fascinated by how different people on the podcast find their way into data, and this episode is no exception. Austin Dowd has always enjoyed photography. He was associated with the American Marketing Association and was always curious in the metrics for his photos. After pestering the analytics person in the AMA in terms of how to analyze his photo metrics, he eventually decided to do a career change into analytics and received a Nanodegree in data analytics through Udemy. This career change coincided with the onset of the pandemic as his photography business started slowing down. We talk about Austin's experiences working for a big conglomerate to a startup, working with messy data, and how photography is just like data visualization because you're telling a story.

Marketing analytics at a startup vs. a big conglomerate

As you can imagine, working for a big company comes with its pros and cons. You have a ton of resources to tackle large problems and projects, but the change management can be quite the process. Multiple teams and stakeholders are involved, and changes can take months to years depending on the type of change you are trying to make.

Source: xkcd

Austin worked at Cox Automotive and talked about how the data stack at Cox Automotive was custom built years ago. That means even small changes to the system were very hard to adjust. Then you have the issue of clients wanting custom edits to their reporting. If you don't have a data engineering team that can build an infrastructure that allows analysts to edit reports on the fly, you'll start getting into being a consulting company providing custom data analytics solutions.

Austin moved to a startup called Blues Wireless where they built a robust data stack, but they didn't necessarily have the marketing team in mind when building out the stack. Product usage analytics were top of mind for the small but budding analytics team. Austin was brought in to coordinate web analytics projects so that the marketing funnel--from a website visitor to a conversion--could be better quantified. Getting accurate data, however, is paramount to this project because you can't make decisions on bad data.

Website data platforms fighting each other

Austin is currently enrolled in a data analytics masters program, and he talked about how cleaning "dirty" data is very different in the academic world vs. the real world. This is true for any discipline, I suppose. In the academic world, the problem space is confined and there are "right" answers, so to speak. In the real world, surprises and nuance are littered all over the place. In the marketing analytics world, you are working with a data format that a Google Analytics or some CRM platform forces upon you. In rare scenarios, cleaning data is as simple as using Python to get rid of a bunch of NULL and N/A values.

Austin realized that page data was being counted twice. I'm sure all of you have dealt with double-counting data and coming up with a system to de-duplicate data. The problem came down to Google Tag Manager fighting with Segment to report on page data for the website. Austin uses GA to view early marketing funnel activities and Segment pulls late marketing funnel activity.